2026年 “AI新贵们”集体押注世界模型

This article breaks down the hottest topic in the AI industry for 2026: the current state where global industry players and capital are collectively betting on world models. Key takeaways are as follows:

1. Core background: The development of existing large language models has hit a bottleneck. Scaling Law effects are slowing, high-quality internet text data is nearly exhausted, and current AI cannot truly understand the rules of the physical world. It suffers from severe hallucination issues in scenarios requiring causal reasoning, making it unfit for core business deployment. The industry urgently needs a new technological breakthrough.

2. Industry status: Multiple players domestically and internationally have already entered the space. Both public and private markets are bullish on world models, and a number of related firms have secured large financing rounds. Internet giants, automakers, and robotics companies have all launched related products, with the sector currently split into four major technical camps: video generation, spatial intelligence, embodied intelligence, and intelligent driving. Each development route has its own strengths and weaknesses.

3. Future trend: The core competition for next-generation AI companies will no longer be over parameters, academic papers, or computing power, but access to real-world scenario resources. Chinese companies, with access to far denser industrial scenarios, hold a clear advantage in this new round of competition.

The rise of world models is driving AI's full penetration into the physical industry, bringing new consumer trends and business upgrading opportunities for brands. Key takeaways are as follows:

1. Shifting consumer trends: AI will gradually penetrate physical consumer scenarios such as automobiles, home robots, spatial design, and XR devices. Consumer demand for intelligent physical experiences will grow rapidly, and segments focused on intelligent physical products will enter a period of strong growth dividends.

2. Business upgrading opportunities: World models solve the hallucination problem that plagued earlier AI systems, enabling brands to pursue intelligent upgrades in product R&D, customer service and other core links. For example, world models can be used for simulation testing during new product development, and offline retail stores can build more intelligent interactive scenarios to improve user experience.

3. Directions for industrial cooperation: With massive capital flooding into the world model track and a wave of new technology startups emerging, brands can align with their own business needs to partner with technology providers early, seize the first-mover advantage in intelligent transformation, and build differentiated competitive edges.

The rise of world models opens up new growth opportunities for AI deployment in the physical industry. Sellers can capitalize on these opportunities and mitigate risks based on their own positioning. Key takeaways are as follows:

1. New incremental opportunities: World models already have multiple clear paths to monetization. Short-term opportunities for quick monetization lie in creative design and spatial services, while mid-term opportunities exist in intelligent driving, and long-term opportunities in robotics and industrial scenarios, with entry points for players of all sizes.

2. Actionable development paths: There is currently no unified technical standard for the industry, and sellers do not need to blindly compete with top players by overinvesting in computing power and parameters. Instead, they can enter the market based on their existing scenario advantages. For example, sellers with existing scenario resources can pursue a lightweight development route and still build strong competitive positioning.

3. Risk warning: World models are still in the early stage of industrial development, with large divergences in technical routes and far from mature technology. Sellers should not blindly go all-in on unproven technologies, and should prioritize deployment tied to their existing business for gradual iteration, to avoid losses from concept hype.

The rise of world models opens up new directions for factories advancing digital and intelligent transformation, and unlocking new business opportunities. Key takeaways are as follows:

1. Product R&D and production: World models are essentially learnable physical simulators. Factories can use them to simulate product performance across different physical environments during the R&D stage, replacing most real-world testing. This can significantly cut R&D costs, shorten development cycles, and improve the success rate of new product development.

2. Untapped business opportunities: China hosts the world's most complete manufacturing ecosystem, with an abundance of real industrial scenarios – which are exactly the most valuable source of physical data and deployment ground for world models. Factories can open their scenarios to partner with AI technology firms, earning data revenue while co-developing intelligent applications tailored for manufacturing.

3. Insights for digital transformation: Factories pursuing AI adoption do not need to blindly pursue large models and massive computing power. Instead, by leveraging their own dense real-world scenarios, they can obtain faster industrial feedback, accelerate engineering iteration, more easily build a data flywheel, and establish differentiated competitive advantages to achieve overtaking on a new curve.

World models have emerged as the core development direction of the current AI industry, bringing new market opportunities for service providers. Key takeaways are as follows:

1. Industry development trend: The AI industry has completed its development phase focused on semantic understanding, and is now shifting toward the physical reasoning stage. Existing large language models cannot meet the demand for deployment in the physical industry, and world models have become a consensus across the industry. They will become the core infrastructure of the physical AI era, extending the industrial chain to create multiple new links including physical data collection, data processing, simulation evaluation, deployment feedback and more, creating massive market opportunities.

2. Core customer pain points: The core pain point for enterprises deploying AI today is the severe hallucination problem of existing large models, which prevents them from being used in core decision-making links. This is particularly acute in embodied intelligence and autonomous driving, where there is a shortage of real training data and low testing efficiency – problems that world models are perfectly positioned to solve.

3. Recommended entry points: Service providers do not need to completely abandon their existing businesses. Instead, they can enter the space by building on their existing advantages. For example, traditional video generation providers can upgrade to offer interactive world models, while existing spatial service providers can build virtual training grounds for embodied intelligence companies. Gradual transformation allows them to capture industrial growth dividends.

The rise of world models brings new demands and challenges to AI platforms, requiring platforms to adjust their operation and layout to adapt to the new trend. Key takeaways are as follows:

1. New industry demands on platforms: World models have a longer technical stack than previous large models. In addition to foundational model training, they require support across multiple links including physical data supply, simulation testing, evaluation, and closed-loop feedback from real-world scenarios. Traditional platforms that only provide basic computing power and model services can no longer meet demand, and need to extend their service chains.

2. Promising layout directions for platforms: Leveraging their existing computing power and data resources, platforms can build full-stack infrastructure for world model development to attract developers working on all technical routes. For example, they can offer simulation testing services for automakers and virtual training scenarios for robotics companies, unlocking new revenue growth points.

3. Risks to avoid: As no unified technical route has emerged for world models yet, platforms should not bet prematurely on a single technical path. Instead, they should build an open ecosystem to support the development of multiple routes, avoid blindly hyping concepts, prioritize onboarding enterprises with real-world deployment capabilities, and nurture closed industrial loops to avoid the risk of a market bubble.

The AI industry is undergoing a major paradigm shift in 2026, with world models emerging as the new core research direction, bringing many new worthwhile research trends and open questions. Key takeaways are as follows:

1. New industry trends: The current AI technology paradigm is shifting from Next Token Prediction to Next State Prediction. Scaling Law for traditional large language models has stopped delivering gains, high-quality text data is approaching depletion, and existing AI cannot solve physical common sense reasoning problems. World models have become the new consensus direction for global industry and academia on the path toward AGI.

2. Open research questions: There is currently no unified definition or technical framework for world models. Different academic and industrial camps have proposed different classification systems, each technical route has its own strengths and weaknesses, and no dominant solution has emerged. Industrial deployment still faces multiple unresolved challenges around data access, engineering implementation, and standard development that require further exploration.

3. Research insights: World models will become the operating system of the physical AI era, connecting underlying hardware to upper-layer industry applications. The core competition for next-generation AI companies is shifting from computing power and model parameters to real-world scenario density and engineering iteration capability. Leveraging its complete manufacturing ecosystem and dense physical industry scenarios, China holds unique advantages in this round of competition, making its industrial development path worthy of in-depth research.